EXPECT_EQ(6 * TF_DataTypeSize(TF_UINT64), TF_TensorByteSize(a));
  EXPECT_EQ(6 * TF_DataTypeSize(TF_UINT64), TF_TensorByteSize(b));

  // Check that a write to one tensor shows up in the other.
  *(static_cast<int64_t*>(TF_TensorData(a))) = 4;
  EXPECT_EQ(4, *(static_cast<int64_t*>(TF_TensorData(b))));
  *(static_cast<int64_t*>(TF_TensorData(b))) = 6;
  EXPECT_EQ(6, *(static_cast<int64_t*>(TF_TensorData(a))));

}

Status ModularRandomAccessFile::Read(uint64 offset, size_t n,
                                     StringPiece* result, char* scratch) const {
  if (ops_->read == nullptr)
    return errors::Unimplemented(
        tensorflow::strings::StrCat("Read() not implemented for ", filename_));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  int64_t read =

                          char* buffer, TF_Status* status) -> int64_t {
    calls++;
    memset(buffer, 'x', n);
    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  std::vector<char> out;
  // Our fake environment is initialized with the current timestamp.
  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
  uint64 now = Env::Default()->NowSeconds();
  env->SetNowSeconds(now);

  if (!status.ok()) GTEST_SKIP() << "CreateDir() not supported: " << status;

  uint64 size;
  status = env_->GetFileSize(dirpath, &size);
  EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::FAILED_PRECONDITION);
}

TEST_P(ModularFileSystemTest, TestGetFileSizeNotFound) {
  const std::string filepath = GetURIForPath("a_dir");
  uint64 size;
  Status status = env_->GetFileSize(filepath, &size);

using tensorflow::string;

namespace {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         tensorflow::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);

}
Status TapeOperation::SetAttrIntList(const char* attr_name,
                                     const int64_t* values, int num_values) {
  forward_op_.attrs.Set(
      attr_name, gtl::ArraySlice<const int64_t>(
                     reinterpret_cast<const int64_t*>(values), num_values));
  return parent_op_->SetAttrIntList(attr_name, values, num_values);
}

  ASSERT_EQ(errors::OK, status_.code()) << status_.message();

  auto ReluGradModel = BuildGradModel(ReluModel, registry_);

  float X_vals[] = {1.0f, 2.0f, 3.0f, -5.0f, -4.0f, -3.0f, 2.0f, 10.0f, -1.0f};
  int64_t X_dims[] = {3, 3};
  AbstractTensorHandlePtr X;
  {
    AbstractTensorHandle* X_raw;
    status_ = TestTensorHandleWithDims<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), X_vals, X_dims, 2, &X_raw);

  virtual Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard
  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the AbstractFunction as a FunctionDef.
  virtual void GetNameAttrList(
      tensorflow::NameAttrList* name_and_attrs) const = 0;

  virtual bool GetInt(absl::string_view, int64_t* result) const = 0;
  virtual bool GetFloat(absl::string_view attr_name, float* result) const = 0;
  virtual bool GetBool(absl::string_view attr_name, bool* result) const = 0;

  EXPECT_EQ(
      1, metrics->point_set_map.at("test/counter")->points.at(0)->int64_value);

  TFE_MonitoringCounterCellIncrementBy(cell, 5);
  EXPECT_EQ(TFE_MonitoringCounterCellValue(cell), 6);
  metrics = collection_registry->CollectMetrics(options);
  EXPECT_EQ(
      6, metrics->point_set_map.at("test/counter")->points.at(0)->int64_value);

  TFE_MonitoringDeleteCounter0(counter);